Opensky

OpenSky provides api to get data for flights in progress. It is rate limited

Code

import requests
import pandas as pd
import geopandas

Code

def fetch_opensky_data():
    url = "https://opensky-network.org/api/states/all"
    response = requests.get(url)
    data = response.json()

    columns = [
        "icao24", "callsign", "origin_country", "time_position", "last_contact",
        "longitude", "latitude", "baro_altitude", "on_ground", "velocity",
        "heading", "vertical_rate", "sensors", "geo_altitude", "squawk",
        "spi", "position_source"
    ]

    df_raw = pd.DataFrame(data['states'], columns=columns)
    return (
        df_raw
        .assign(
            time_position=pd.to_datetime(df_raw["time_position"], unit="s"),
            last_contact=pd.to_datetime(df_raw["last_contact"], unit="s"),
        )
    )

df_flights = fetch_opensky_data()


df_flihts_non_nan = df_flights.dropna(subset=["longitude", "latitude"])
gdf = geopandas.GeoDataFrame(
    df_flihts_non_nan,
    geometry=geopandas.points_from_xy(df_flihts_non_nan.longitude, df_flihts_non_nan.latitude),
    crs="EPSG:4326",
)

gdf.explore(fullscreen=True, tooltip=False, popup=True)

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Flights in world at notebook time

Number of flights by origin_country

Code

(
    df_flights
    .groupby("origin_country")
    .agg(num_flights=("icao24", "count"))
    .sort_values("num_flights", ascending=False)
    .head(20)
)

	num_flights
origin_country
United States	6756
Canada	392
United Kingdom	368
Ireland	299
Germany	220
Turkey	187
France	182
Malta	173
Spain	164
Austria	146
Kingdom of the Netherlands	128
Brazil	118
India	112
United Arab Emirates	103
China	101
Mexico	97
Switzerland	87
Poland	79
Republic of Korea	57
Sweden	57

Top 20 countries with most flights

All flights

For reference

Code

df_flights

	icao24	callsign	origin_country	time_position	last_contact	longitude	latitude	baro_altitude	on_ground	velocity	heading	vertical_rate	sensors	geo_altitude	squawk	spi	position_source
0	a8a812	N657PT	United States	2025-05-15 19:50:21	2025-05-15 19:50:21	-106.4123	45.8444	13106.40	False	241.80	269.51	0.33	None	13167.36	4077	False	0
1	a7250f	N56FA	United States	2025-05-15 19:49:01	2025-05-15 19:49:03	-118.4882	34.2084	213.36	False	8.76	356.63	0.00	None	198.12	None	False	0
2	39de4b		France	NaT	2025-05-15 19:50:21	NaN	NaN	10546.08	False	220.75	131.98	9.10	None	10706.10	0656	False	0
3	39de4a	TVF18AN	France	2025-05-15 19:50:21	2025-05-15 19:50:21	10.0075	47.7024	10355.58	False	242.35	262.44	-0.33	None	10401.30	1000	False	0
4	39de4d	TVF54UR	France	2025-05-15 19:50:21	2025-05-15 19:50:22	1.9363	48.5843	1165.86	False	94.89	60.79	0.00	None	1272.54	0745	False	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10908	a6d83c	DAL2921	United States	2025-05-15 19:50:21	2025-05-15 19:50:21	-112.0365	40.8568	2232.66	False	117.60	291.29	4.88	None	2255.52	6034	False	0
10909	a4cbd6	N408RF	United States	2025-05-15 19:50:21	2025-05-15 19:50:21	-79.9957	33.1121	617.22	False	76.76	135.27	-1.63	None	609.60	None	False	0
10910	a9bc8a	N7262X	United States	2025-05-15 19:48:55	2025-05-15 19:48:55	-84.3605	35.5589	457.20	False	35.29	306.70	-3.90	None	449.58	2260	False	0
10911	ad00be	N937MA	United States	2025-05-15 19:50:21	2025-05-15 19:50:21	-83.9749	39.6819	609.60	False	51.82	60.89	-1.63	None	563.88	1200	False	0
10912	c00734	WJA1480	Canada	2025-05-15 19:50:21	2025-05-15 19:50:21	-104.0575	37.3548	11277.60	False	237.52	140.45	-0.65	None	11460.48	3376	False	0

10913 rows × 17 columns